In this paper, a cognitive system based on a nonlinear neural controller and intelligent algorithm that will guide an autonomous mobile robot during continuous path-tracking and navigate over solid obstacles with avoidance was proposed. The goal of the proposed structure is to plan and track the reference path equation for the autonomous mobile robot in the mining environment to avoid the obstacles and reach to the target position by using intelligent optimization algorithms. Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) Algorithms are used to finding the solutions of the mobile robot navigation problems in the mine by searching the optimal paths and finding the reference path equation of the optimal

It is well-known that the existence of outliers in the data will adversely affect the efficiency of estimation and results of the current study. In this paper four methods will be studied to detect outliers for the multiple linear regression model in two cases :  first, in real data; and secondly,  after adding the outliers to data and the attempt to detect it. The study is conducted for samples with different sizes, and uses three measures for  comparing between these methods . These three measures are : the mask, dumping and standard error of the estimate.

In this paper we estimate the coefficients and scale parameter in linear regression model depending on the residuals are of type 1 of extreme  value distribution for the largest values . This can be regard as an improvement for the studies with the smallest values . We study two estimation methods ( OLS  & MLE ) where we resort to Newton – Raphson (NR) and Fisher Scoring methods to get MLE estimate because the difficulty of using the usual approach with MLE . The relative efficiency criterion is considered beside to the statistical inference procedures for the extreme value regression model of type 1 for largest values . Confidence interval , hypothesis testing for both scale parameter and regression coefficients

Background: Globally, hepatitis B is one of the most common infectious diseases. Estimates indicate that at least 2 billion people have been infected with the hepatitis B virus (HBV), with more than 378 million people being chronic carriers. Those individuals at higher risk for acquiring HBV and transmitting disease like pregnant women should be screened for hepatitis B surface antigen (HBsAg) to prevent transmission by vaccination and operation. Aim of study: The aim of this study was to determine the prevalence of HBsAg and its associated parameters in pregnant women who referred to antenatal clinic in Baghdad Province. Methods: The 234 apparently healthy pregnant women and their families, husbands and children were se

Background: Migraine is one of multiple attack neurological conditions that causes moderate to severe headaches with no defined pathophysiology and few animal models. Aim: Establishing an animal model that reproduces migraine-like action is important in medical research to identify the mechanism underlying this disorder. Additionally, it facilitates the availability and reliability of new models that may act as human surrogate models. Method: Rabbits were divided into four groups. Negative group, migraine group, rizatriptan- nitroglycerin group, and rizatriptan group. The frequency of head scratching and the histopathological changes in the brain, liver, kidney, and heart for groups were evaluated in all groups. Results: T

In this paper, we deal with games of fuzzy payoffs problem while there is uncertainty in data. We use the trapezoidal membership function to transform the data into fuzzy numbers and utilize the three different ranking function algorithms. Then we compare between these three ranking algorithms by using trapezoidal fuzzy numbers for the decision maker to get the best gains

Within the framework of big data, energy issues are highly significant. Despite the significance of energy, theoretical studies focusing primarily on the issue of energy within big data analytics in relation to computational intelligent algorithms are scarce. The purpose of this study is to explore the theoretical aspects of energy issues in big data analytics in relation to computational intelligent algorithms since this is critical in exploring the emperica aspects of big data. In this chapter, we present a theoretical study of energy issues related to applications of computational intelligent algorithms in big data analytics. This work highlights that big data analytics using computational intelligent algorithms generates a very high amo